发表机构
The University of Hong Kong (HKU)（香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对太阳能收集的空间生成式AI的计算-通信权衡，提出联合资源优化框架，其策略可动态平衡图像生成质量与传输可靠性，性能优于静态基线。
AI 中文摘要
卫星正成为将生成式人工智能（AI）服务扩展到缺乏地面基础设施的偏远地区的有前景平台。然而，部署空间生成式人工智能从根本上受到太阳能收集（EH）提供的有限且随时间变化的星载能量的约束。本文提出了一种太阳能驱动的空间生成式人工智能框架，其中卫星接收用户提示、执行基于扩散的图像生成模型，并在严格的时间窗口内下行链路压缩后的结果。我们确定了由共享的收集能量预算决定的基本计算-通信（C²）权衡。具体而言，增加生成步骤数可提高固有图像质量，但会消耗下行链路传输可用的能量和时间；而优先考虑通信可保证可靠传输，但会牺牲语义质量。为平衡这些权衡并最大化端到端（E2E）生成性能，我们利用由确定性轨道运动引起的可预测太阳能EH动态，并采用易于处理的两步方法开发了联合C²资源优化框架。首先，我们表征了在连续太阳能EH下固定生成深度的最大下行链路吞吐量，这确立了将等待时间选择与最优发射功率控制解耦的分离原理。接下来，我们制定了联合C²效用最大化问题，并在主导恒定功率机制下推导了闭式、低复杂度的步骤选择策略。在现实轨道动态下的大量实验表明，所提出的策略可动态平衡生成质量和传输可靠性，在不同太阳能EH状态下，相较于以静态计算和通信为中心的基线，该策略取得了显著的E2E性能提升。
英文摘要
Satellites are emerging as promising platforms to extend generative \emph{artificial intelligence} (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative AI is fundamentally constrained by the limited, time-varying onboard energy supplied by solar \emph{energy harvesting} (EH). This paper presents a framework for solar-powered space generative AI in which a satellite receives a user prompt, executes a diffusion-based image-generation model, and downlinks the compressed result within a strict time window. We identify the fundamental \emph{computation--communication} (C$^2$) trade-offs governed by the shared harvested-energy budgets. Specifically, increasing the number of generation steps improves intrinsic image quality but depletes energy and time available for downlink transmission, whereas prioritizing communication guarantees reliable delivery but sacrifices semantic quality. To balance these trade-offs and maximize \emph{end-to-end} (E2E) generative performance, we exploit the predictable solar-EH dynamics induced by deterministic orbital motion and develop a joint C$^2$ resource-optimization framework using a tractable two-step approach. First, we characterize the maximum downlink throughput for a fixed generation depth under continuous solar EH. This establishes a separation principle that decouples waiting-time selection from optimal transmit-power control. Next, we formulate a joint C$^2$ utility-maximization problem and derive a closed-form, low-complexity step-selection policy in the dominant constant-power regime. Extensive experiments under realistic orbital dynamics demonstrate that the proposed policy dynamically balances generation quality and transmission reliability. This yields significant E2E performance gains over static computation- and communication-centric baselines across diverse solar-EH states.
Comments14 pages, 11 figures
